Title :
A parsimonious constraint-based algorithm to induce Bayesian network structures from data
Author :
Cruz-Ramirez, Nicandro ; Nava-Fernandez, Luis ; Mesa, Hector Gabriel Acosta ; Martinez, Erandi Barrientos ; Rojas-Marcial, Juan Efraín
Author_Institution :
Dept. of Artificial Intelligence, Univ. Veracruzana, Mexico
Abstract :
In this paper, we present a novel algorithm, called MP-Bayes, which induces Bayesian network structures from data based on entropy measures. One of the main features of this method is its parsimonious nature: it tends to represent the joint probability distribution underlying the data with the least number of arcs. While other methods that build Bayesian networks tend to overfit the data, MP-Bayes creates models that seem to have an adequate trade-off between accuracy and complexity. To support such a claim, we compare the performance of MP-Bayes, in terms of classification, against those of four different Bayesian network classifiers. The results show that our procedure generalizes well in a wide range of situations.
Keywords :
Bayes methods; belief networks; computational complexity; constraint handling; entropy; pattern classification; Bayesian network classifier; Bayesian network structure; MP-Bayes; entropy; parsimonious constraint-based algorithm; probability distribution; Algorithm design and analysis; Artificial intelligence; Bayesian methods; Entropy; Probability distribution; Random variables; Search engines; Simulated annealing; Statistical analysis; Testing;
Conference_Titel :
Computer Science, 2005. ENC 2005. Sixth Mexican International Conference on
Print_ISBN :
0-7695-2454-0